Crime in Montgomery County¶

250px-Map_of_Maryland_highlighting_Montgomery_County.svg.png

Out of the 24 counties in the state of Maryland, Montgomery County is the most populous. It boasts the 20th highest household income in the country, and interestingly, the highest percentage of residents over the age of 25 who hold post-graduate degrees (Source). High household income and education indicate that Montgomery County could be a good place to live - but they aren't the only things to consider.

When evaluating a place to work/live/visit, safety is paramount, and one of the largest components of safety is crime. Montgomery County is a large geographical area, covering 507 square miles with a population of over 1 million residents, and therefore it is not sufficient to simply look at overall crime rate statistics to determine if it's a safe place to live. We asked ourselves the following questions:

  • What trends in the crime rates point towards whether Montgomery County is becoming a safer place to live over the past several years?
  • Has the Montgomery County police department been able to influence crime rates through specific actions or initiatives?
  • What impact did COVID have on crime in Montgomery County?
  • What sub-regions of Montgomery County are safer to live in, and why?

In order to answer these questions, we embarked on a data analysis journey to determining whether Montgomery County is a "safe" place to live, hoping to find evidence that crime was trending downward, the police department was effective at containing and addressing crime, and if there are any demographic factors that contribute to different patterns within the county.

Table of Contents¶

  • Project Overview
  • Data Acquisition
  • Exploratory Data Analysis
  • Data Cleaning
  • Glossary

The libraries that we will use for the project are <....>.

First, we will need to import all required libraries and set up prerequisites.

In [1]:
# Import core packages
import pandas as pd
import numpy as np
import re
import datetime as dt
from matplotlib import pyplot as plt
import seaborn as sns
import requests
from matplotlib import animation
from matplotlib.animation import FuncAnimation
from IPython.display import HTML, Javascript

# Import packages for visualization
import folium
from folium import plugins
from folium.plugins import MarkerCluster
from folium.plugins import HeatMapWithTime

%matplotlib widget

Data¶

The Montgomery County government has a comprehensive collection of datasets that report on county demographics, businesses, public safety, and more. From that, we were able to find a dataset that had the potential to answer our questions in the form of a csv file that we can download from their website.

This dataset is updated daily with new crime reports, and contains data that spans from 2017 to the present. It contains 282,000 rows, and has 30 columns containing detailed information about each incident - including start time, number of victims, location, and type of crime. From this plethora of data we will be able to drill down to the most important variables and indicators that we will use to answer our questions.

We downloaded the final version that is used in our analysis on November 08, 2023.

In [2]:
# Load Montgomery dataset
montgomery_crime_df = pd.read_csv('Montgomery_Crime.csv', index_col = ['Incident ID'], dtype={'PRA': str})

Exploratory Data Analysis¶

The first step of this process is to examine our dataset and understand its main characteristics, which will help us formulate some initial assumptions. This will help us identify any obvious errors, identify patterns, detect outliers, and lay the foundation for the data cleaning tasks that will need to be done.

In [3]:
# Inspect first 5 observations
montgomery_crime_df.head(5)
Out[3]:
Offence Code CR Number Dispatch Date / Time Start_Date_Time End_Date_Time NIBRS Code Victims Crime Name1 Crime Name2 Crime Name3 ... Location Council Districts Councils Communities Zip Codes Municipalities Council Districts_from_i23j_3mj8 Service Regions Montgomery County Boundary Council Districts 7
Incident ID
201452140 1205 230065034 11/08/2023 03:20:12 AM 11/08/2023 03:20:00 AM NaN 120 1 Crime Against Property Robbery ROBBERY - STREET-OTHER WEAPON ... (38.9918, -77.0241) 5.0 5.0 7.0 3065.0 1.0 5.0 1.0 1.0 7.0
201452138 2203 230065033 11/08/2023 02:43:30 AM 11/08/2023 02:30:00 AM 11/08/2023 02:42:00 AM 220 1 Crime Against Property Burglary/Breaking and Entering BURGLARY - FORCED ENTRY-NONRESIDENTIAL ... (39.055, -77.1183) 3.0 3.0 19.0 60.0 1.0 3.0 3.0 1.0 7.0
201452136 1305 230065028 11/08/2023 01:08:41 AM 11/08/2023 12:08:00 AM 11/08/2023 01:50:00 AM 13A 1 Crime Against Person Aggravated Assault ASSAULT - AGGRAVATED - NON-FAMILY-OTHER WEAPON ... (39.0777, -77.14) 3.0 3.0 25.0 60.0 12.0 3.0 3.0 1.0 4.0
201452119 2601 230065015 11/07/2023 09:15:52 PM 11/07/2023 09:15:00 PM NaN 26A 1 Crime Against Property False Pretenses/Swindle/Confidence Game FRAUD - CONFIDENCE GAME ... (39.0834, -77.0652) 4.0 4.0 59.0 6.0 1.0 4.0 4.0 1.0 2.0
201452132 1304 230065019 11/07/2023 09:06:46 PM 11/07/2023 09:06:00 PM 11/08/2023 12:00:00 AM 13A 5 Crime Against Person Aggravated Assault ASSAULT - AGGRAVATED - NON-FAMILY-GUN ... (39.1418, -77.2244) 3.0 3.0 36.0 33.0 21.0 3.0 5.0 1.0 4.0

5 rows × 38 columns

In [4]:
# Calculate descriptive statistics about numerical variables in dataset
montgomery_crime_df.describe()
Out[4]:
Offence Code CR Number Victims Zip Code Address Number Latitude Longitude Council Districts Councils Communities Zip Codes Municipalities Council Districts_from_i23j_3mj8 Service Regions Montgomery County Boundary Council Districts 7
count 279992.000000 2.799920e+05 279992.000000 277598.000000 2.533180e+05 279992.000000 279992.000000 276220.000000 276220.000000 273817.000000 277334.000000 275385.000000 276220.000000 274351.000000 274100.0 276237.000000
mean 2802.817538 1.785562e+08 1.028054 20877.247509 8.530961e+03 38.709476 -76.380807 3.246278 3.246278 27.601161 421.351381 4.186887 3.246278 3.437713 1.0 4.069082
std 1193.342610 6.111787e+07 0.209805 163.822120 1.799520e+04 3.790282 7.478188 1.387858 1.387858 16.085272 995.212405 6.534121 1.387858 1.413078 0.0 2.018775
min 901.000000 1.004668e+07 1.000000 6.000000 0.000000e+00 0.000000 -77.516700 1.000000 1.000000 1.000000 6.000000 1.000000 1.000000 1.000000 1.0 1.000000
25% 2303.000000 1.800087e+08 1.000000 20854.000000 1.700000e+03 39.021810 -77.198300 2.000000 2.000000 16.000000 21.000000 1.000000 2.000000 2.000000 1.0 2.000000
50% 2399.000000 1.900553e+08 1.000000 20878.000000 8.200000e+03 39.072190 -77.098400 3.000000 3.000000 25.000000 39.000000 1.000000 3.000000 4.000000 1.0 4.000000
75% 2999.000000 2.100518e+08 1.000000 20904.000000 1.240000e+04 39.142190 -77.029700 5.000000 5.000000 39.000000 71.000000 1.000000 5.000000 5.000000 1.0 6.000000
max 9103.000000 2.301015e+08 22.000000 29878.000000 2.090600e+06 39.342600 0.000000 5.000000 5.000000 62.000000 3065.000000 24.000000 5.000000 5.000000 1.0 7.000000
In [5]:
# Display shape of dataset
df_shape = montgomery_crime_df.shape
In [6]:
# Display column types
df_column_types = montgomery_crime_df.dtypes
In [7]:
# Identify columns with missing values
missing_columns = montgomery_crime_df.columns[montgomery_crime_df.isnull().any()]
print("Columns with missing values:")
print(montgomery_crime_df[missing_columns].isnull().sum())

Columns with missing values:
Dispatch Date / Time                 40628
End_Date_Time                       139934
Police District Name                   476
Block Address                        26773
City                                    13
State                                 5226
Zip Code                              2394
PRA                                      6
Address Number                       26674
Street Prefix                       267418
Street Name                            477
Street Suffix                       275349
Street Type                            785
Council Districts                     3772
Councils                              3772
Communities                           6175
Zip Codes                             2658
Municipalities                        4607
Council Districts_from_i23j_3mj8      3772
Service Regions                       5641
Montgomery County Boundary            5892
Council Districts 7                   3755
dtype: int64

We want to analyze the different types of crimes that are committed, and potentially do further categorization, so we first need to determine the existing categorization provided by this dataset.

In [8]:
# Display value counts for crime types to gather initial information about segments
montgomery_crime_df['Crime Name1'].value_counts()
Out[8]:
Crime Name1
Crime Against Property    175277
Crime Against Society      67030
Crime Against Person       37685
Name: count, dtype: int64
In [9]:
# Display value counts for sub-category of crime type
montgomery_crime_df['Crime Name2'].value_counts()
Out[9]:
Crime Name2
Theft From Motor Vehicle                       31259
Simple Assault                                 26114
Drug/Narcotic Violations                       25234
Destruction/Damage/Vandalism of Property       23503
Shoplifting                                    21444
All other Larceny                              17028
Driving Under the Influence                    16797
Theft from Building                            13527
Burglary/Breaking and Entering                 10670
Identity Theft                                 10357
Motor Vehicle Theft                            10178
Theft of Motor Vehicle Parts or Accessories     9251
False Pretenses/Swindle/Confidence Game         8143
Liquor Law Violations                           6999
Disorderly Conduct                              6367
Credit Card/Automatic Teller Machine Fraud      6137
Aggravated Assault                              5428
Trespass of Real Property                       4467
Robbery                                         4344
Counterfeiting/Forgery                          3756
Weapon Law Violations                           2932
Drug Equipment Violations                       2314
Forcible Fondling                               2158
Forcible Rape                                   1709
Impersonation                                   1665
Family Offenses, NonViolent                      923
Pocket/picking                                   878
Forcible Sodomy                                  730
Embezzlement                                     562
Extortion/Blackmail                              548
Purse-snatching                                  508
Sexual Assault With An Object                    506
Intimidation                                     495
Arson                                            440
Pornography/Obscene Material                     385
Bad Checks                                       362
Wire Fraud                                       337
Stolen Property Offenses                         263
Prostitution                                     206
Murder and Nonnegligent Manslaughter             153
Peeping Tom                                      134
Drunkenness                                      117
Statuory Rape                                    108
Purchasing Prostitution                           97
Kidnapping/Abduction                              96
Animal Cruelty                                    94
From Coin/Operated Machine or Device              72
Human Trafficking, Commercial Sex Acts            66
Welfare Fraud                                     43
Curfew/Loitering/Vagrancy Violations              38
Assisting or Promoting Prostitution               22
Justifiable Homicide                              14
Incest                                             7
Bribery                                            2
Negligent Manslaughter                             2
Human Trafficking, Involuntary Servitude           2
Operating/Promoting/Assisting Gambling             1
Name: count, dtype: int64
In [10]:
# Display value counts for final sub-category of crime type
montgomery_crime_df['Crime Name3'].value_counts()
Out[10]:
Crime Name3
LARCENY - FROM AUTO                                   31259
ASSAULT - 2ND DEGREE                                  21998
LARCENY - SHOPLIFTING                                 21444
DRUGS - MARIJUANA - POSSESS                           16486
DRIVING UNDER THE INFLUENCE LIQUOR                    15967
                                                      ...  
BRIBERY - OFFERING BRIBE                                  1
HOMICIDE - JUSTIFIABLE                                    1
HOMICIDE - JOHN OR JANE DOE - NO WARRANT                  1
HOMOSEXUAL ACT WITH MAN                                   1
COMM SEX OFF - TRANSPORT FEMALE INTERSTATE FOR IMM        1
Name: count, Length: 268, dtype: int64

Data Cleaning¶

We have several columns with thousands of observations that are null. There is also the potential that our data may be corrupted, improperly formatted, duplicated, or incomplete. In order to rectify these issues, we will need to clean this dataset prior to performing any analyses, as inaccurate data can lead to unreliable algorithms/results.

Identify and Remove Duplicate Observations¶

Before we proceed with handling the null data, we should determine if there are any duplicates. We will do this by looking specifically at the Incident ID - duplicates may have resulted from errors in data entry, data extraction, or other data collection processes.

In [11]:
# Identify duplicate records based on the Incident ID
duplicate_records = montgomery_crime_df.index.duplicated()

#montgomery_crime_df[duplicate_records]
# Display the number of duplicate records
print('Number of duplicate records: ', montgomery_crime_df.index.duplicated().sum())

Number of duplicate records:  25371
In [12]:
# Remove duplicated records, keeping the first instance because df is sorted in descending order by time
montgomery_crime_df = montgomery_crime_df[~montgomery_crime_df.index.duplicated(keep='first')]
# montgomery_crime_df

Remove Unnecessary Columns¶

We will remove several columns from our dataset for the following reasons.

Police District Number: The information about the police district is already captured in the 'Police District Name' column. Including both columns is redundant, and the 'Police District Name' provides sufficient information for analysis.

Zip Codes: The 'Zip Code' column already contains information about the location. Including both 'Zip Code' and 'Zip Codes' is redundant, and 'Zip Code' is more relevant for location-based analysis.

Municipalities: The dataset already includes the 'City' column, which represents the municipality or city associated with each incident. Including 'Municipalities' is redundant and does not provide additional meaningful information.

Council Districts_from_i23j_3mj8: The purpose and origin of this column are not explicitly defined, and its inclusion does not contribute to the analysis. It lacks clear relevance or significance for the crime-related insights sought from the dataset.

Service Regions: The role and significance of 'Service Regions' in the context of crime analysis are not apparent. Without a clear understanding of its relevance, this column is excluded from the dataset.

Council Districts 7: The dataset already contains 'Council Districts,' and the additional 'Council Districts 7' column lacks justification or documentation for its inclusion. It is prudent to rely on the more standard 'Council Districts' column for analysis.

In [13]:
# Drop unnecessary columns
montgomery_crime_df.drop(['Police District Number','Zip Codes', 'Municipalities', 'Council Districts_from_i23j_3mj8', 'Service Regions', 'Council Districts 7'], axis=1, inplace=True)
# montgomery_crime_df

Correct Incorrect City Values¶

Add markdown....

There are multiple cities that are mispelled in our dataset. Because we plan to use this column in our analysis, we need to figure out how to fix the incorrect values.

In [14]:
# Replace the NaN values with an empty string for consistency
montgomery_crime_df['City'] = montgomery_crime_df['City'].fillna('')

# Define a mapping for corrections
city_corrections = {
    'SILVER SPRING': ['SILVER SPRIN G', 'SILVERS SPRING', 'SILER SPRING', 'SILVE SPRING', 'SILVERSPRING', 'SILVER APRING', 'SILVER SPRIG', 'SILVER SPRING`', 'SLVER SPRING', 'SLIVER SPRING', 'SILV ER SPRING', 'SILVE4R SPRING', 'SILVER SPRING`', 'SILVER SPRNIG', 'SILVER SPRIING', 'SILVER SPRIND', 'SILVER SPRIGN'],
    'ROCKVILLE': ['ROCKVIILE', 'ROCKVIL', 'ROCKVIILE', 'ROCKVILEE', 'ROCKVILLE,', 'ROCKIVLLE', 'ROCKVILLE,', 'ROCKVIILLE', 'ROCVILLE', 'ROCVKILLE', 'ROCKVIILE'],
    'GAITHERSBURG': ['GAITHERBURG', 'GAITHERBURG', 'GAITHERSBUG', 'GAITHERSBUIRG', 'GAITHERSBURT', 'GAITHERSBRUG', 'GAITHERSGURG', 'GAITHESRBURG', 'GAITERSBURG', 'GAITHERSRBURG', 'GAIHTERSBURG', 'GAUTHERSBURG', 'GAIHTERSBURG', 'GAIHERSBURG', 'GATIHERSBURG', 'GAITHERBSURG', 'GAITHERSBRG', 'GAITHERSBRG', 'GAITHERSURG', 'GAITHRESBURG', 'GAISTHERSBURG', 'GAITHERSURG', 'GITHERSBURG', 'GITHERSBYRG'],
    'BETHESDA': ['BEHTESDA', 'BEHESDA', 'BETHEDA', 'BETHESDAS', 'BETHESDAS', 'BETHSDA', 'BETESDA', 'BETHESDA'],
    'TAKOMA PARK': ['TACOMA PARK', 'TAKOMA PARK'],
    # Add more entries as needed
}
city_corrections_manual = {
    'BETHESDA' : ['BEHTESDA', 'BEHESDA', 'BETESDA', 'BETHEDA', 'BETHESA', 'BETHESDAS', 'BETHSDA'],
    'Beallsville': ['BEALSVILLE'],
    'BARNESVILLE' : ['BARNESVIILE','BARNESVIILE','BARNSVILLE'],
    'BROOKEVILLE' : 'BROOKVILLE',
    'BURTONSVILLE' : ['BURTOSNVILLE','BURTSONVILLE', 'BUTINSVILLE'],
    'CHEVY CHASE': ['CEHVY CHASE', 'CHVEY CHASE', 'CHEVY CHASE #3', 'CHEVY CHASE #4', 'CHEVY CHASE VIEW', 'CHEVY CHASE VILLAGE'],
    'CLARKSBURG': ['CALARKSBURG', 'CLAEKSBURG','CLARKESBURG','CLARKSURG', 'CLARSBURG','CLARSKBURG'],
    'COMUS': 'COMUS',
    'DAMASCUS': 'DANASCUS',
    'DERWOOD' : 'DEERWOOD',
    'FREDERICK': 'FREDERICK',
    'FRIENDSHIP HEIGHTS': 'FRIENDHSIP HEIGHTS',
    'GAITHERSBURG' : ['7','GATIHERSBURG', 'GAIHTERSBURG', 'GAITHERBURG', 'GAITHERESBURG','GAITEHRSBURG', 'GITHERSBURG', 'GAIHERSBURG', 'GAISTHERSBURG', 'GAITERSBURG', 'GAITHERBSURG', 'GAITHERSBRG', 'GAITHERSBRUG', 'GAITHERSBUG', 'GAITHERSBUIRG', 'GAITHERSBURT', 'GAITHERSBYRG', 'GAITHERSGURG', 'GAITHERSRBURG', 'GAITHERSSBURG', 'GAITHERSURG', 'GAITHESBURG', 'GAITHESRBURG', 'GAITHRERSBURG', 'GAITHRESBURG', 'GIATHERSBURG', 'GATHERSBURG', 'GAUTHERSBURG'],
    'GARRETT PARK' : 'GARRETT PARK',
    'GERMANTOWN' : ['4','GRMANTOWN', 'GEMANTOWN', 'GERMATOWN', 'GERMANTNOWN', 'GERMANTOWM', 'GERMANTOOWN', 'GERMANTOEN', 'GERMANTONW', 'GERMANTIWN', 'GEERMANTOWN', 'GERAMNTOWN', 'GERANTOWN', 'GERMAN4TOWN', 'GERMANTOW', 'GERMANTOWNMD','GERMANTWN', 'GERMANTWON', 'GERMNATOWN', 'GERMNTOWN', 'GERRMANTOWN'],
    'GLEN ECHO': 'GLEN ECHO`',
    'HYATTSTOWN': ['HYATTTOWN','1'],
    'KENSINGTON': ['KENSIGNTON', 'KENSINGTOWN', 'KENSONGTON', 'KENSTINGTON', 'KENNSINGTON', 'KENSINGTNO'],
    'LAYTONSVILLE': 'LATONSVILLE',
    'MONTGOMERY VILLAGE': ['20877','6','MCG','MCGGAITHERSBURG', 'MONT VILLAGE', 'MONTGGOMERY VILLAGE', 'MONTGOMERY VILAGE','MONTGOMERY VILLAE', 'MONTGOMERY VILLLAGE', 'MONTGOMRY VILLAGE', 'MONTOMGERY VILLAGE','MOTGOMERY VILLAGE', 'MOMTGOMERY VILLAGE', 'MONGTOMERY VILLAGE'],
    'MOUNT AIRY': ['MT AIRY', 'MT. AIRY'],
    'NORTH BETHESDA': ['N BETHESDAQ', 'N. BETHESDA', 'N. POTOMAC', 'NORTH BEHTESDA', 'NORTH BETHSDA','NORTH POTOAMC', 'NOTRTH POTOMAC', 'N BETHESDA'],
    'NORTH POTOMAC' : 'N POTOMAC',
    'OLNEY': ['ONLEY','ONEY'],
    'POOLESVILLE': ['POOLSVILLE','PO'],
    'POTOMAC': 'POTIMAC',
    'ROCKVILLE': ['3','ROCKIVLLE', 'ROCKVILLLE', 'ROCKVIILLE', 'ROCVILLE', 'ROCVKILLE', 'RCKVILLE', 'ROCKILLE', 'ROCKIVILLE', 'ROCKVIILE', 'ROCKVILEE', 'ROCKVILL', 'ROCKVLLE', 'ROKVILLE', 'ROOCKVILLE', "ROCKVILLE'", 'ROCKVILLE,','ROCKVILE'],
    'SANDY SPRING': 'SANDY SPPRING',
    'SILVER SPRING': ['2','SILVER  SPRING','SLIVER SPRING','SILVE4R SPRING', 'SILVER', 'SILVER APRING', 'SILVER SPING', 'SILVER SPRIING', 'SILVER SPRIND', 'SILVER SPRING', 'SILVER SPRINGQ', 'SILVER SPRIGN','SILVER SRING', 'SILVER SRING', 'SIVLER SPRING', 'SLVER SPRING', 'SIVER SPRING', 'SILER SPRING', 'SILV ER SPRING', 'SILVE SPRING', 'SILVER SPRING', 'SILVER SPIRNG','SILVER SPRIG', 'SILVER SPRIN', 'SILVER SPRIN G', 'SILVER SPRING`', 'SILVER SPRNG','SILVER SPRNIG', 'SILVER SPSRING', 'SILVER SRPING', 'SILVERS SPRING', 'SILVERSPRING','SILVR SPRING'],
    'SPENCERVILLE': 'APENCERVILLE',
    'TAKOMA PARK': ['TAKOMS PARK','TACOMA PARK', 'TAKOMA','TP'],
    'VALLEYWOOD': 'VALLEYWOOD',
    'WHEATON': ['WEHATON','WEATON']

}

# not sure we need to display this
# city_corrections_manual.keys()
In [15]:
# Function to correct misspelled city names
def correct_city_name(city):
    for correct_name, misspelled_names in city_corrections_manual.items():
        if city in misspelled_names:
            return correct_name
    return city  # Return the original city name if not found in the misspelled names

# Apply the correction function to the 'City' column and display results
montgomery_crime_df['City'] = montgomery_crime_df['City'].apply(correct_city_name)
#print(len(montgomery_crime_df['City'].unique()))
#print(sorted(montgomery_crime_df['City'].unique()))

There are some corrupted values, specifically in the Dispatch Date/Time, Start_Date_Time, and Agency columns, that are not feasible to impute. Instead, we will remove them from the dataset.

In [16]:
# Filter out rows where either dispatch date/time or start date time are missing
montgomery_crime_df = montgomery_crime_df[montgomery_crime_df['Dispatch Date / Time'].notnull() & montgomery_crime_df['Start_Date_Time'].notnull()]

# Filter out rows where dispatch date/time is prior to start date time
montgomery_crime_df = montgomery_crime_df[montgomery_crime_df['Dispatch Date / Time'] >= montgomery_crime_df['Start_Date_Time']]

# Drop rows where Agency = P, since this is a meaningless value 
montgomery_crime_df.drop(montgomery_crime_df[montgomery_crime_df['Agency'] == 'P'].index, inplace=True)
montgomery_crime_df.drop(montgomery_crime_df[montgomery_crime_df['Agency'] == 'OTHR'].index, inplace=True)

Changing column types - Dispatch, Start & End Time¶

Some of our columns are not the desired types. Lets change that for all 3 columns that represent a timestamp of a crime observation - start, dispatch, and end.

In [17]:
# Convert 'Dispatch Date / Time' to datetime format
montgomery_crime_df['Dispatch Date / Time'] = pd.to_datetime(montgomery_crime_df['Dispatch Date / Time'])

# Convert 'Start_Date_Time' to datetime format
montgomery_crime_df['Start_Date_Time'] = pd.to_datetime(montgomery_crime_df['Start_Date_Time'])

# Convert 'End_Date_Time' to datetime format
montgomery_crime_df['End_Date_Time'] = pd.to_datetime(montgomery_crime_df['End_Date_Time'])

Lastly, our data covers crimes ranging from 2016 to November 8th, 2023. We decided to filter out any crime data that occurred prior to 2016...

NEED TO CONSIDER FILTER OUT PRIOR TO APRIL 2017 BECAUSE CRIME RATES WERE VERY LOW¶

In [18]:
# filter out any observations that occurred prior to 2017 
montgomery_crime_df = montgomery_crime_df[montgomery_crime_df['Start_Date_Time'] > '2017-01-01']
Out[18]:
Offence Code CR Number Dispatch Date / Time Start_Date_Time End_Date_Time NIBRS Code Victims Crime Name1 Crime Name2 Crime Name3 ... Street Name Street Suffix Street Type Latitude Longitude Location Council Districts Councils Communities Montgomery County Boundary
Incident ID
201452140 1205 230065034 2023-11-08 03:20:12 2023-11-08 03:20:00 NaT 120 1 Crime Against Property Robbery ROBBERY - STREET-OTHER WEAPON ... FENTON NaN ST 38.99175 -77.0241 (38.9918, -77.0241) 5.0 5.0 7.0 1.0
201452138 2203 230065033 2023-11-08 02:43:30 2023-11-08 02:30:00 2023-11-08 02:42:00 220 1 Crime Against Property Burglary/Breaking and Entering BURGLARY - FORCED ENTRY-NONRESIDENTIAL ... ROCKVILLE NaN PIK 39.05503 -77.1183 (39.055, -77.1183) 3.0 3.0 19.0 1.0
201452119 2601 230065015 2023-11-07 21:15:52 2023-11-07 21:15:00 NaT 26A 1 Crime Against Property False Pretenses/Swindle/Confidence Game FRAUD - CONFIDENCE GAME ... BIRCHTREE NaN LA 39.08343 -77.0652 (39.0834, -77.0652) 4.0 4.0 59.0 1.0
201452132 1304 230065019 2023-11-07 21:06:46 2023-11-07 21:06:00 2023-11-08 00:00:00 13A 5 Crime Against Person Aggravated Assault ASSAULT - AGGRAVATED - NON-FAMILY-GUN ... QUINCE ORCHARD NaN BLV 39.14175 -77.2244 (39.1418, -77.2244) 3.0 3.0 36.0 1.0
201452133 3615 230065007 2023-11-07 20:54:34 2023-11-07 20:54:00 NaT 90C 1 Crime Against Society Disorderly Conduct SEX OFFENSE - INDECENT EXPOSURE TO ADULT ... FREDERICK NaN AVE 39.14886 -77.2077 (39.1489, -77.2077) 3.0 3.0 36.0 1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
201368726 2502 220011575 2022-03-17 18:44:08 2017-01-01 07:00:00 2022-03-17 16:49:00 250 1 Crime Against Property Counterfeiting/Forgery FORGERY OF OTHER ... MONTROSE NaN AVE 39.02585 -77.1008 (39.0258, -77.1008) 1.0 1.0 12.0 1.0
201181181 2610 180015001 2018-03-27 15:04:07 2017-01-01 06:00:00 2017-12-31 00:00:00 26F 1 Crime Against Property Identity Theft FRAUD - IDENTITY THEFT ... BRASSIE NaN PL 39.17418 -77.2022 (39.1742, -77.2022) 2.0 2.0 38.0 1.0
201133509 2604 170506789 2017-05-09 10:46:29 2017-01-01 00:01:00 2017-01-01 23:59:00 26C 1 Crime Against Property Impersonation FRAUD - IMPERSONATION ... CHESHIRE NaN DR 39.02616 -77.1119 (39.0262, -77.1119) 1.0 1.0 19.0 1.0
201136683 2604 170510782 2017-05-30 21:51:31 2017-01-01 00:01:00 2017-05-30 21:00:00 26C 1 Crime Against Property Impersonation FRAUD - IMPERSONATION ... MAIN NaN ST 39.28768 -77.2017 (39.2877, -77.2017) 2.0 2.0 51.0 1.0
201163464 2604 170544271 2017-11-22 10:03:32 2017-01-01 00:01:00 2017-09-30 23:59:00 26C 1 Crime Against Property Impersonation FRAUD - IMPERSONATION ... GREY COLT NaN DR 39.08376 -77.2363 (39.0838, -77.2363) 2.0 2.0 24.0 1.0

186980 rows × 32 columns

Now that we've done that, we want to perform some grouping operations that will be used later on in our analysis. The Start Time, which we have defined as when a crime is reported by either a citizen or the police, is what we will focus on as it is the best representation of "when" a crime occurred.

In [19]:
# Extract month and year information from 'Start_Date_Time'
montgomery_crime_df['Month'] = montgomery_crime_df['Start_Date_Time'].dt.month
montgomery_crime_df['Year'] = montgomery_crime_df['Start_Date_Time'].dt.year

# Mapping numerical months to corresponding names for readability
month_names = {1: 'January', 2: 'February', 3: 'March', 4: 'April', 5: 'May', 6: 'June', 7: 'July',
              8: 'August', 9: 'September', 10: 'October', 11: 'November', 12: 'December'}

# Define the order of months for categorical ordering
month_order = [month for month in month_names.values()]

# Create a new column 'Month_Name' and map the month names
montgomery_crime_df['Month_Name'] = montgomery_crime_df['Month'].map(month_names)

# Set 'Month_Name' as a categorical variable w|ith the specified order
montgomery_crime_df['Month_Name'] = pd.Categorical(montgomery_crime_df['Month_Name'], categories=month_order, ordered=True)

# Group by 'Year' and 'Month_Name', and count the occurrences of crimes
incident_groups = montgomery_crime_df.groupby(['Year', 'Month_Name'], observed=True)['Crime Name1'].count()

NOT SURE THIS IS NECESSARY¶

A different lens through which we can look at this data about when crimes occured is the time of day, or the hour at which they occur. To do this, we will do some data transformation.

In [20]:
# NOT SURE THIS IS NECESSARY
# Extract the hour information from 'Start_Date_Time'
montgomery_crime_df['Hour'] = montgomery_crime_df['Start_Date_Time'].dt.hour

# Group by hour and count the occurrences of crimes
crime_by_hour = montgomery_crime_df.groupby('Hour').size()

Overall Crime Rates in Montgomery County¶

The first thing we will look at is the overall crime rate in Montgomery, and how it has changed over time. In order to do this, we will need to transform our data to have it represent time series data that we can visualize. We decided to analyze it by total crime volume each month.

To answer our questions about crime trends in Montgomery County, we should also analyze the time series data for different categories of crime. We will do this by creating a time series line graph that plots the total number of crimes taking place on a month by month basis. We will also take a cross section of the data for each of the 3 sub-categories of crime - Crime Against Person, Crime Against Property, and Crime Against Society.

Crime Against Person is defined as , and includes assault, homicide, and robbery. Crime Against Property is defined as , and includes breaking & entering, car theft, and . Crime Against Society is defined as, and includes .

In [21]:
# Create dummy variables for crime category
crime_name_dummies = pd.get_dummies(montgomery_crime_df['Crime Name1'], prefix='dummy', prefix_sep='_', dummy_na=False, columns=['Crime Name1'], dtype='int')

# merge dummy variables with main dataframe
montgomery_crime_df = montgomery_crime_df.merge(crime_name_dummies, on='Incident ID');
montgomery_crime_df
Out[21]:
Offence Code CR Number Dispatch Date / Time Start_Date_Time End_Date_Time NIBRS Code Victims Crime Name1 Crime Name2 Crime Name3 ... Councils Communities Montgomery County Boundary Month Year Month_Name Hour dummy_Crime Against Person dummy_Crime Against Property dummy_Crime Against Society
Incident ID
201452140 1205 230065034 2023-11-08 03:20:12 2023-11-08 03:20:00 NaT 120 1 Crime Against Property Robbery ROBBERY - STREET-OTHER WEAPON ... 5.0 7.0 1.0 11 2023 November 3 0 1 0
201452138 2203 230065033 2023-11-08 02:43:30 2023-11-08 02:30:00 2023-11-08 02:42:00 220 1 Crime Against Property Burglary/Breaking and Entering BURGLARY - FORCED ENTRY-NONRESIDENTIAL ... 3.0 19.0 1.0 11 2023 November 2 0 1 0
201452119 2601 230065015 2023-11-07 21:15:52 2023-11-07 21:15:00 NaT 26A 1 Crime Against Property False Pretenses/Swindle/Confidence Game FRAUD - CONFIDENCE GAME ... 4.0 59.0 1.0 11 2023 November 21 0 1 0
201452132 1304 230065019 2023-11-07 21:06:46 2023-11-07 21:06:00 2023-11-08 00:00:00 13A 5 Crime Against Person Aggravated Assault ASSAULT - AGGRAVATED - NON-FAMILY-GUN ... 3.0 36.0 1.0 11 2023 November 21 1 0 0
201452133 3615 230065007 2023-11-07 20:54:34 2023-11-07 20:54:00 NaT 90C 1 Crime Against Society Disorderly Conduct SEX OFFENSE - INDECENT EXPOSURE TO ADULT ... 3.0 36.0 1.0 11 2023 November 20 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
201368726 2502 220011575 2022-03-17 18:44:08 2017-01-01 07:00:00 2022-03-17 16:49:00 250 1 Crime Against Property Counterfeiting/Forgery FORGERY OF OTHER ... 1.0 12.0 1.0 1 2017 January 7 0 1 0
201181181 2610 180015001 2018-03-27 15:04:07 2017-01-01 06:00:00 2017-12-31 00:00:00 26F 1 Crime Against Property Identity Theft FRAUD - IDENTITY THEFT ... 2.0 38.0 1.0 1 2017 January 6 0 1 0
201133509 2604 170506789 2017-05-09 10:46:29 2017-01-01 00:01:00 2017-01-01 23:59:00 26C 1 Crime Against Property Impersonation FRAUD - IMPERSONATION ... 1.0 19.0 1.0 1 2017 January 0 0 1 0
201136683 2604 170510782 2017-05-30 21:51:31 2017-01-01 00:01:00 2017-05-30 21:00:00 26C 1 Crime Against Property Impersonation FRAUD - IMPERSONATION ... 2.0 51.0 1.0 1 2017 January 0 0 1 0
201163464 2604 170544271 2017-11-22 10:03:32 2017-01-01 00:01:00 2017-09-30 23:59:00 26C 1 Crime Against Property Impersonation FRAUD - IMPERSONATION ... 2.0 24.0 1.0 1 2017 January 0 0 1 0

186980 rows × 39 columns

In [22]:
# Create new column that does not contain time values
montgomery_crime_df['Day'] = montgomery_crime_df['Start_Date_Time'].dt.date

# Count the number of crimes in each month 
group_by_date = montgomery_crime_df.groupby('Day', observed=True)['Crime Name1'].count()
group_by_date.index = pd.to_datetime(group_by_date.index)
group_by_month = group_by_date.resample('1M').sum()

# Sum the number of crimes against person for each month in dataset
person_group_by_date = montgomery_crime_df.groupby('Day')['dummy_Crime Against Person'].sum()
# Convert to datetime 
person_group_by_date.index = pd.to_datetime(group_by_date.index)
# Resample dataset to aggregate counts based on month
person_group_by_month = person_group_by_date.resample('1M').sum()
# Drop final month since it is incomplete
person_group_by_month.drop(person_group_by_month.tail(1).index,inplace=True)

# Sum the number of crimes against person for each month in dataset
property_group_by_date = montgomery_crime_df.groupby('Day')['dummy_Crime Against Property'].sum()
# Convert to datetime 
property_group_by_date.index = pd.to_datetime(group_by_date.index)
# Resample dataset to aggregate counts based on month
property_group_by_month = property_group_by_date.resample('1M').sum()
# Drop final month since it is incomplete
property_group_by_month.drop(property_group_by_month.tail(1).index,inplace=True)

# Sum the number of crimes against person for each month in dataset
society_group_by_date = montgomery_crime_df.groupby('Day')['dummy_Crime Against Society'].sum()
# Convert to datetime 
society_group_by_date.index = pd.to_datetime(group_by_date.index)
# Resample dataset to aggregate counts based on month
society_group_by_month = society_group_by_date.resample('1M').sum()
# Drop final month since it is incomplete
society_group_by_month.drop(society_group_by_month.tail(1).index,inplace=True)

# Drop the final value in the total crime rate series 
group_by_month.drop(group_by_month.tail(1).index,inplace=True)
In [23]:
# Initialize values for use in our animated plot 
crime_rate = group_by_month.values
t = group_by_month.index
x,y = [], []

person_crime_rate = person_group_by_month.values
y1 = []

property_crime_rate = property_group_by_month.values
y2 = []

society_crime_rate = society_group_by_month.values
y3 = []
In [24]:
%%capture 

# create matplotlib figure and axes
fig = plt.figure(figsize=(14,8));
axes = fig.add_subplot(1,1,1);

# set initial values and configuration
axes.set_ylim(0, 3000)
axes.set_title('Montgomery County Crime Rate', fontsize=20)
axes.set_xlabel('Month/Year')
axes.set_ylabel('Number of Crimes')
crime_rate_plot = plt.plot([], [])

# create legend for plot
all_crime = plt.plot(x,y, color='black', marker='*', scaley=True, scalex=True, label='All Crime')
person_crime = plt.plot(x,y1, 'r-.', scaley=True, scalex=True, label='Crime Against Person')
property_crime = plt.plot(x,y2, 'b--', scaley=True, scalex=True, label='Crime Against Property')
society_crime = plt.plot(x,y3, 'g:', scaley=True, scalex=True, label='Crime Against Society')

legend = plt.legend(loc=1)
In [25]:
# Define function that will animate the plot
def animate(i):
    # append new values to the arrays that will be plotted
    x.append(t[i])
    y.append(crime_rate[i])
    y1.append(person_crime_rate[i])
    y2.append(property_crime_rate[i])
    y3.append(society_crime_rate[i])

    # plot crime rates
    all_crime = plt.plot(x,y, color='black', marker='*', scaley=True, scalex=True)
    person_crime = plt.plot(x,y1, 'r-.', scaley=True, scalex=True)
    property_crime = plt.plot(x,y2, 'b--', scaley=True, scalex=True)
    society_crime = plt.plot(x,y3, 'g:', scaley=True, scalex=True)

    # ADD ARROWS AND ANIMATIONS
    if i > 40:
        # plot marker for first US case of covid 19
        plt.text(dt.datetime.strptime('03/10/2019', '%m/%d/%Y'), 900, 'First Covid-19\ncase reported\nin U.S.', color='grey', fontsize='large', alpha=0.1)
        plt.plot([dt.datetime.strptime('01/20/2020', '%m/%d/%Y')] * 100, range(0, 4000, 40), '--', linewidth=1, alpha=0.01)
        # plot marker for official start of covid pandemic
        plt.text(dt.datetime.strptime('03/20/2020', '%m/%d/%Y'), 800, 'WHO declares\nCovid-19 Pandemic', color='grey', fontsize='large', alpha=0.1)
        plt.plot([dt.datetime.strptime('03/11/2020', '%m/%d/%Y')] * 100, range(0, 4000, 40), '--', linewidth=1, alpha=0.01)
In [26]:
# create animation using animate function
anim = FuncAnimation(fig=fig, func=animate, frames=len(group_by_month), interval=400, repeat=False)

# show plot and enable viewing in html 
display(HTML(anim.to_jshtml()))

# Automatically play the animation once it is generated in HTML
Javascript('document.querySelector(".anim-buttons > button:nth-child(6)").click()')
Out[26]:

MARKDOWN FOR RESULTS/OUTCOMES FROM VISUALIZATION

Mapping Crime in Montgomery County¶

Instead of utilizing plotly for the geographical data, we have chosen to use folium since it provides a more detailed output of the map. The area in which the crime took place can be seen much more clearly because of the details of the surrounding area that is being provided. Also, the higher level of interactivity allows us to explore the data more effectively.

Since it takes a while to display all of the crimes in the dataset, we are going to use a subset of 1000 randomly sampled crimes to display in the first map. However, for the second visualization, we will utilize all data values otherwise the map itself will be quite sparse and not show any significant patterns that can be used for analysis.

When splitting up the different major cities for the geographical analysis, there are some of the cities that do not have boundaries. So, instead of using city boundaries, the council district boundaries of montgomery county will be utilized instead. This still provides a good overview of the crimes by each city.

Major Cities in Each District:

  • District 1: Potomac, Bethesda
  • District 2: Germantown, Poolesville, Darnestown, Martinsville, Barnesville
  • District 3: Rockville, Gaithersburg
  • District 4: North Bethesda, Kensington, Silver Spring
  • District 5: Colesville, Fairland, White Oak, Four Corners
  • District 6: Aspen Hill, Glenmont, Wheaton
  • District 7: Damascus, Montgomery Village, Olney

Source

In [27]:
%%capture

# Randomly sample the dataset for visualization
#crime = montgomery_crime_df[:1000]
crime = montgomery_crime_df.sample(n=100)

# Create the starting map for analysis
mont_crime_map = folium.Map(location=[39.05, -77.15], zoom_start=10)

# Mark every crime in montgomery county
crime.apply(lambda row: folium.CircleMarker(location=[row.loc['Latitude'], row.loc['Longitude']],
                 popup=row.loc['Crime Name2'], radius=5, weight=0, fill_color='red',
                tooltip='click').add_to(mont_crime_map), axis=1)

# Create layers for all districts
all_districts = folium.FeatureGroup(name='All Districts')
mont_crime_map.add_child(all_districts)

d1 = plugins.FeatureGroupSubGroup(all_districts, 'District 1')
mont_crime_map.add_child(d1)

d2 = plugins.FeatureGroupSubGroup(all_districts, 'District 2')
mont_crime_map.add_child(d2)

d3 = plugins.FeatureGroupSubGroup(all_districts, 'District 3')
mont_crime_map.add_child(d3)

d4 = plugins.FeatureGroupSubGroup(all_districts, 'District 4')
mont_crime_map.add_child(d4)

d5 = plugins.FeatureGroupSubGroup(all_districts, 'District 5')
mont_crime_map.add_child(d5)

d6 = plugins.FeatureGroupSubGroup(all_districts, 'District 6')
mont_crime_map.add_child(d6)

d7 = plugins.FeatureGroupSubGroup(all_districts, 'District 7')
mont_crime_map.add_child(d7)


# Add all of the district geojson layers for analysis
folium.GeoJson('cd_1.geojson', name='Council District 1').add_to(d1)
folium.GeoJson('cd_2.geojson', name='Council District 2').add_to(d2)
folium.GeoJson('cd_3.geojson', name='Council District 3').add_to(d3)
folium.GeoJson('cd_4.geojson', name='Council District 4').add_to(d4)
folium.GeoJson('cd_5.geojson', name='Council District 5').add_to(d5)
folium.GeoJson('cd_6.geojson', name='Council District 6').add_to(d6)
folium.GeoJson('cd_7.geojson', name='Council District 7').add_to(d7)

# Create layer control to adjust geographical analysis as needed
folium.LayerControl().add_to(mont_crime_map)

To determine the distribution of crime in relation to the police stations, we will include markers on the map that represent the police stations in Montgomery County.

In [28]:
# Loading in the data for the police stations
police = pd.read_csv('montgomery_pd.csv')
police[['Latitude', 'Longitude']] = police['Location'].str.split(',', expand=True).astype(float)

# Distinguish the police stations from crime data
police_icon = 'https://github.com/aniruddhashelke/montgomery_pd/blob/main/download.png'

# Mark every police station and adding it to the map for analysis
for index, row in police.iterrows():
    folium.Marker(
        location=[row['Latitude'], row['Longitude']],
        popup=row['Police Department'],
        icon=folium.Icon(icon='star', color = 'blue')
    ).add_to(mont_crime_map)

mont_crime_map
Out[28]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Creating the Heatmap visualization¶

In [29]:
# Create a heatmap for all the years
example = montgomery_crime_df.copy()
example.head()
Out[29]:
Offence Code CR Number Dispatch Date / Time Start_Date_Time End_Date_Time NIBRS Code Victims Crime Name1 Crime Name2 Crime Name3 ... Communities Montgomery County Boundary Month Year Month_Name Hour dummy_Crime Against Person dummy_Crime Against Property dummy_Crime Against Society Day
Incident ID
201452140 1205 230065034 2023-11-08 03:20:12 2023-11-08 03:20:00 NaT 120 1 Crime Against Property Robbery ROBBERY - STREET-OTHER WEAPON ... 7.0 1.0 11 2023 November 3 0 1 0 2023-11-08
201452138 2203 230065033 2023-11-08 02:43:30 2023-11-08 02:30:00 2023-11-08 02:42:00 220 1 Crime Against Property Burglary/Breaking and Entering BURGLARY - FORCED ENTRY-NONRESIDENTIAL ... 19.0 1.0 11 2023 November 2 0 1 0 2023-11-08
201452119 2601 230065015 2023-11-07 21:15:52 2023-11-07 21:15:00 NaT 26A 1 Crime Against Property False Pretenses/Swindle/Confidence Game FRAUD - CONFIDENCE GAME ... 59.0 1.0 11 2023 November 21 0 1 0 2023-11-07
201452132 1304 230065019 2023-11-07 21:06:46 2023-11-07 21:06:00 2023-11-08 00:00:00 13A 5 Crime Against Person Aggravated Assault ASSAULT - AGGRAVATED - NON-FAMILY-GUN ... 36.0 1.0 11 2023 November 21 1 0 0 2023-11-07
201452133 3615 230065007 2023-11-07 20:54:34 2023-11-07 20:54:00 NaT 90C 1 Crime Against Society Disorderly Conduct SEX OFFENSE - INDECENT EXPOSURE TO ADULT ... 36.0 1.0 11 2023 November 20 0 0 1 2023-11-07

5 rows × 40 columns

In [32]:
# Create an index for the heatmap
example['Year_2'] = pd.DatetimeIndex(example['Start_Date_Time']).year.astype('int64')
example['Month_2'] = pd.DatetimeIndex(example['Start_Date_Time']).month.astype('int64')
example['Day'] = pd.DatetimeIndex(example['Start_Date_Time']).day.astype('int64')

example['Day_Month_Year'] =  example['Month_2'].apply(str) + '/' + example['Day'].apply(str) + '/' + example['Year_2'].apply(str)
#example['Day_Month_Year'] = pd.to_datetime(example['Day_Month_Year'], infer_datetime_format=True)
example['Day_Month_Year'] = pd.to_datetime(example['Day_Month_Year'])
In [33]:
# Display every single crime date on the heatmap
coordinates = []
for x in example['Day_Month_Year'].sort_values().unique():
    new_list = []
    for index, value in example[example['Day_Month_Year'] == x].iterrows():
        new_list.append([value['Latitude'], value['Longitude']])
    coordinates.append(new_list)
In [34]:
# time index for heatmap
time_idx = []
for i in example['Day_Month_Year'].astype('datetime64[us]').sort_values().unique():
    time_idx.append(i)

# format the index 
formatted = [x.strftime('%m/%d/%Y') for x in time_idx]
In [35]:
# plot the heatmap
crime_heatmap = folium.Map(location=[39.05, -77.15], zoom_start=10,
                          tiles='cartodbdark_matter',)


HeatMapWithTime(coordinates, radius=10, auto_play=True, position='bottomright',
                index=formatted, max_opacity=0.7).add_to(crime_heatmap)

crime_heatmap
Out[35]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Montgomery County Police Department¶

The Montgomery County Department of Police, located in Montgomery County, Maryland, includes approximately 1,300 sworn officers and 650 support personnel.

Analyzing the efficiency of Law Enforcement¶

By using crime log times, we can make an interesting analysis on the efficiency of law enforcement in the County. There are records for end time in the dataset which would indicate the time and date when the investigation was marked to be completed. The empty records are not really useless, as they indicate whether the agency was able to complete the investigation or not, so we did not filter them out.

We want to classify the various police agencies in Montgomery County according to their performance on percentages of solved crimes.

In [36]:
# Filtering out unsolved crimes, null values and bad entries
montgomery_crime_df['Solved'] = ~(montgomery_crime_df['End_Date_Time'].isnull() | (montgomery_crime_df['End_Date_Time'] == 0))

# calculating solved percentage and grouping it by Agency
solved_percentage = montgomery_crime_df.groupby('Agency')['Solved'].mean() * 100

# Making a bar plot 
plt.figure(figsize=(10, 6))
bars = solved_percentage.plot(kind='bar', color='skyblue')
plt.title('Percentage of Solved Crimes by Agency')
plt.xlabel('Agency')
plt.ylabel('Percentage of Solved Crimes (%)')
plt.xticks(rotation=45)
plt.ylim(0, 100)  # Set y-axis limit from 0 to 100 for percentage
plt.grid(axis='y')

# Adding percentage values over bars
for index, value in enumerate(solved_percentage):
    plt.text(index, value + 1, f'{value:.2f}%', ha='center', color='black', fontsize=9)
plt.tight_layout()
plt.show()
Figure

Conclusion¶

what we learned

what we recommend

remaining questions?

References¶

Councilmanic. Montgomery County GIS Open Data 2.0, MD. (n.d.). https://opendata-mcgov-gis.hub.arcgis.com/datasets/mcgov-gis::councilmanic/explore?location=39.142744%2C-77.208800%2C10.87

Wikimedia Foundation. (2023, November 25). Montgomery County, Maryland. Wikipedia. https://en.wikipedia.org/wiki/Montgomery_County,_Maryland

Glossary¶

Sector/Beat: a specific area or geographical territory to which a patrol officer is assigned (beat is a subdivision of sector).

Crime Start Time: when a crime is reported, either to or by police.

Crime End Time: when the case for a crime is closed by police.

Dispatch Time: when a police office/unit is sent to the scene of a crime.